This paper introduces a novel method for generating Typical Meteorological Year (TMY) data for microgrids that employ bifacial photovoltaic modules. The method applies Principal Component Analysis (PCA) to five meteorological parameters that influence the power output of bifacial modules: ambient temperature, Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI), albedo, and snow depth. The method selects the most representative months from a 10-year historical data set based on the correlation between the principal components and the original data. The performance of the proposed method is compared with existing methods; Sandia method and NSRDB TMY Method (TMY3), in terms of capturing the interannual variability of the meteorological data and minimizing the average deviation. The comparison is based on the z-score metric and the interannual energy gap, which measure the deviation and the variability of the annual energy yield of a microgrid with 60 bifacial modules. The results show that the proposed method yields a lower z-score (0.20) and a smaller interannual energy gap (5.3%) than the other methods, indicating a closer alignment with the average interannual energy and a higher reliability and accuracy of the optimal sizing and design of microgrids that use bifacial modules.